Real Time 3D Internal Building Directory Map
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Abstract
Global Positioning System (GPS) is a famous technology around the world in identifying the real time precise location of any object with the assistance of satellites. The most common application of GPS is the use of outdoor maps. GPS offers efficient, scalable and cost-effective location services. However, this technology is not reliable when the position is in an indoor environment. The signal is very weak or totally lost due to signal attenuation and multipath effects. Among the indoor positioning technologies, WLAN is the most convenient and cost effective. In recent research, machine learning algorithms have become popular and utilized in wireless indoor positioning to achieve better performance. In this paper, different machine learning algorithms are employed to classify different positions in the real-world environment (e.g., Ixora Apartment - House and Multimedia University Malacca – FIST building). Received Signal Strength Indication (RSSI) is collected at each reference point. This data is then used to train the model with hyperparameter tuning. Based on the experiment result, Random Forest achieved 82% accuracy in Ixora Apartment and 84% accuracy in one of the buildings in Multimedia University Malacca. These results outperformed the other models, i.e., K-Nearest Neighbors (KNN) and Support Vector Machine (SVM).
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References
M. Nabati and S. A. Ghorashi, “A real-time fingerprint-based indoor positioning using deep learning and preceding states,” Expert Syst Appl, vol. 213, Mar. 2023, doi: 10.1016/j.eswa.2022.118889.
J. F. Raquet, “Navigation Using Pseudolites, Beacons, and Signals of Opportunity,” NATO STO lecture series SET-197, navigation sensors and systems in GNSS degraded and denied environments, pp. 1–18, 2013.
P. Spachos and K. N. Plataniotis, “BLE Beacons for Indoor Positioning at an Interactive IoT-Based Smart Museum,” IEEE Syst J, vol. 14, no. 3, pp. 3483–3493, Sep. 2020, doi: 10.1109/JSYST.2020.2969088.
Z. Zuo, L. Liu, L. Zhang, and Y. Fang, “Indoor positioning based on bluetooth low-energy beacons adopting graph optimization,” Sensors (Switzerland), vol. 18, no. 11, Nov. 2018, doi: 10.3390/s18113736.
L. Pei, J. Liu, Y. Chen, R. Chen, and L. Chen, “Evaluation of fingerprinting-based WiFi indoor localization coexisted with Bluetooth,” The Journal of Global Positioning Systems, vol. 15, no. 1, Dec. 2017, doi: 10.1186/s41445-017-0008-x.
A. A. Kalbandhe and S. C. Patil, “Indoor Positioning System using Bluetooth Low Energy,” in International Conference on Computing, Analytics and Security Trends, CAST 2016, Institute of Electrical and Electronics Engineers Inc., Apr. 2017, pp. 451–455. doi: 10.1109/CAST.2016.7915011.
Y. B. Bai, S. Wu, H. Wu, and K. Zhang, “Overview of RFID-Based Indoor Positioning Technology,” GSR, vol. 2012, 2012.
C.-S. Wang, C.-L. Chen, and Y.-M. Guo, A real-time indoor positioning system based on RFID and Kinect. Springer, 2013.
J. D. Domingo, C. Cerrada, E. Valero, and J. A. Cerrada, “An improved indoor positioning system using RGB-D cameras and wireless networks for use in complex environments,” Sensors (Switzerland), vol. 17, no. 10, Oct. 2017, doi: 10.3390/s17102391.
S. Lee, J. Kim, and N. Moon, “Random forest and WiFi fingerprint-based indoor location recognition system using smart watch,” Human-centric Computing and Information Sciences, vol. 9, no. 1, Dec. 2019, doi: 10.1186/s13673-019-0168-7.
A. Nightingale, “Triangulation,” International Encyclopedia of Human Geography, pp. 489–492, Jan. 2009, doi: 10.1016/B978-008044910-4.00552-6.
M. E. Rusli, M. Ali, N. Jamil, and M. M. Din, “An Improved Indoor Positioning Algorithm Based on RSSI-Trilateration Technique for Internet of Things (IOT),” in Proceedings - 6th International Conference on Computer and Communication Engineering: Innovative Technologies to Serve Humanity, ICCCE 2016, Institute of Electrical and Electronics Engineers Inc., Dec. 2016, pp. 12–77. doi: 10.1109/ICCCE.2016.28.
L. Nomdedeu, J. Sales, R. Marin, E. Cervera, and J. Aez, Sensing capabilities for mobile robotics. Elsevier, 2011.
L. S. De Oliveira, O. K. Rayel, and P. Leitao, “Low-Cost Indoor Localization System Combining Multilateration and Kalman Filter,” in IEEE International Symposium on Industrial Electronics, Institute of Electrical and Electronics Engineers Inc., Jun. 2021. doi: 10.1109/ISIE45552.2021.9576353.
R. Abishek, K. Abishek, N. Hariharan, M. R. Vaideeswaran, and C. S. Paripooranan, Analysis Of Machine Learning Algorithms For Wi-Fi-based Indoor Positioning System. IEEE, 2019.
J. Choliz, A. Hernandez, and A. Valdovinos, “A framework for UWB-based communication and location tracking systems for wireless sensor networks,” Sensors, vol. 11, no. 9, pp. 9045–9068, Sep. 2011, doi: 10.3390/s110909045.
A. Alarifi et al., “Ultra wideband indoor positioning technologies: Analysis and recent advances,” Sensors (Switzerland), vol. 16, no. 5. MDPI AG, May 01, 2016. doi: 10.3390/s16050707.
D. Tian and Q. Xiang, “Research on Indoor Positioning System Based on UWB Technology,” pp. 662–665, 2020.
P. Dabove, V. Di Pietra, M. Piras, A. A. Jabbar, and S. A. Kazim, “Indoor positioning using Ultra-wide band (UWB) technologies: Positioning accuracies and sensors’ performances,” in 2018 IEEE/ION Position, Location and Navigation Symposium, PLANS 2018 - Proceedings, Institute of Electrical and Electronics Engineers Inc., Jun. 2018, pp. 175–184. doi: 10.1109/PLANS.2018.8373379.
Q. Yang, S. Zheng, M. Liu, and Y. Zhang, “Research on Wi-Fi indoor positioning in a smart exhibition hall based on received signal strength indication,” EURASIP J Wirel Commun Netw, vol. 2019, no. 1, Dec. 2019, doi: 10.1186/s13638-019-1601-3.
Y. Wu, R. Chen, W. Li, Y. Yu, H. Zhou, and K. Yan, “Indoor Positioning Based on Walking-Surveyed Wi-Fi Fingerprint and Corner Reference Trajectory-Geomagnetic Database,” IEEE Sens J, vol. 21, pp. 18964–18977, 2021.
G. Huang, Z. Hu, J. Wu, H. Xiao, and F. Zhang, “WiFi and vision-integrated fingerprint for smartphone-based self-localization in public indoor scenes,” IEEE Internet Things J, vol. 7, pp. 6748–6761, 2020.
L. Lau et al., “An autonomous ultra-wide band-based attitude and position determination technique for indoor mobile laser scanning,” ISPRS Int J Geoinf, vol. 7, no. 4, Apr. 2018, doi: 10.3390/ijgi7040155.
P.-W. Chin, K.-W. Ng, and N. Palanichamy, “Plant Disease Detection and Classification Using Deep Learning Methods: A Comparison Study,” Journal of Informatics and Web Engineering, vol. 3, no. 1, pp. 155–168, Feb. 2024, doi: 10.33093/jiwe.2024.3.1.10.
T. Ahmed Khan, R. Sadiq, Z. Shahid, M. M. Alam, and M. Mohd Su’ud, “Sentiment Analysis using Support Vector Machine and Random Forest,” Journal of Informatics and Web Engineering, vol. 3, no. 1, pp. 67–75, Feb. 2024, doi: 10.33093/jiwe.2024.3.1.5.